Power Shortage Research Articles

A planning model for coal power exit scales based on minimizing idle and shortage losses: A case study of China

Owing to the rising energy demand and coal dependency in developing countries, the losses incurred from coal power exit are substantial, leading to a low willingness to exit and a high risk of tragedy of the commons. Concurrently, the normalization of peak loads and increasing penetration of renewable energy have exacerbated the contradiction between the overall electricity surplus and peak power shortages in these countries. In response to these challenges, we propose an optimal planning model for a coal power exit scale based on the minimization of losses from idle coal power and industrial power shortages. The main findings are presented as follows: First, compared with the coal power exit models guided by the energy trilemma, our model can reduce losses (the primary demand in developing countries) by at least 22 %, and, in some scenarios, it can increase the exit scale by up to 147 GW. Second, unlike existing methods that measure the overall impact of power shortages, our approach is better at capturing industries' production loss characteristics, which is important for developing countries (positioned in the production segment of the industrial chain), including the evolution patterns of resource and time losses, recovery trajectories of power interruptions, and main influencing factors. Third, China's optimal coal power exit scale for 2030 fluctuates between −7 and 282 GW, with total losses between 48.34 and 188.86 billion yuan. This study provides quantitative tools and decision-making references for optimizing coal power exit and power use.

High Performance Solar-Driven Power-Water Cogeneration for Practical Application: From Micro/Nano Materials to Beyond.

Solar-driven water-electricity cogeneration is a promising strategy for tackling water scarcity and power shortages. However, comprehensive reviews on performance, scalability, commercialization, and power density are lacking. This Perspective presents an overview of recent developments and insights into the challenges and future outlooks for practical applications in this area. We summarize recent advances in high-efficiency water production, focusing on rapid evaporation and condensation. Then we categorize power-water cogeneration systems by power generation mechanisms like steam, evaporation, salinity gradient, photovoltaics, and temperature gradient, providing a comprehensive summary of the performance and applicability of these systems in different scenarios. Finally, we highlight challenges in current systems, considering nanoscale mechanisms and large-scale manufacturing, while also exploring potential trends for future practical applications.

DESIGN AND SIMULATION OF PV-POWERED MOTORPUMP RECIRCULATING WATER SYSTEM IN RICE FIELD CRAB FARM USING MPPT-BASED NEUROFUZZY AND BIDIRECTIONAL CONVETER CHARGER CONTROLLERS

Community enterprises engaged in rice field crab farming face significant challenges due to the absence of an electricity grid, resulting in energy shortages that hinder the operation of a 24-hour recirculating water system, essential for maintaining optimal conditions during the crab laviculture phase. Consequently, crab larva survival rates have experienced a significant 60% decline. To address this issue, this paper introduces the design and simulation of an off-grid photovoltaic solar-powered motor-driven water pumping system (PVWPS) using maximum power point tracking (MPPT) and charger controllers, to meet the power demands. The proposed MPPT-based neuro-fuzzy controller (NFC), in conjunction with a proportional-integral-derivative (PID) controller, maximizes the utilization of PV power for the motor pump. Additionally, a bidirectional converter facilitates battery charging and discharging. The motor speed, which directly influences the required pump flow rate, is regulated by controlling the voltage across the DC-linked capacitor, using an additional proportional-integral (PI) controller. The Kp, Ki, and Kd parameters of both the PID and PI controllers are meticulously adjusted through a genetic algorithm (GA) to optimize control performance. Simulation results demonstrate the effectiveness of the implemented MPPT-NFC and charge controller. The energy utilization efficiency can be increased by up to 97% and the overall efficiency of the PVWPS is improved by up to 10.5%, effectively overcoming the power shortage challenge. Consequently, the proposed PVWPS, utilizing MPPT-NFC with PID-controller and a bidirectional converter PI-controller, enables 24-hour operation of the recirculating water system, with the anticipation of an increased crab larva survival rate.

Life cycle sustainability assessment of waste-to-electricity plants for 2030 power generation development scenarios in western Lombok, Indonesia under multi-criteria decision-making approach

Globally, the dual challenges of power shortages and waste management underscore the critical need for innovative and sustainable energy solutions. Addressing these issues is essential for achieving environmental sustainability, economic development, and social well-being. This paper proposes an integrated life cycle sustainability assessment (LCSA) of waste-to-electricity (WtE) technologies for the future energy mix in Western Lombok, Indonesia which strongly faces power shortage and waste management challenges. The proposed LCSA assesses three WtE technologies, including gasification, incineration, and landfill gas (LFG) with 5 environmental, 3 social, and 4 economic indicators to estimate the final sustainability score under a multi-criteria decision-making framework. Also, a comparative analysis from the LCSA perspective for different WtE technologies, along with coal, and diesel power plants is conducted as a decision support tool in the 2030 power capacity development scenarios. To indicate the effectiveness and flexibility of the proposed model, the robust decision-making analysis using different important weights on sustainability aspects is investigated. Results show LFG is the most sustainable technology among all options scoring around 0.78. However, coal with a levelized cost of 0.42 $/MWh, and diesel with a payback period of 1.75 years are much more economical options for western Lombok. Sensitivity analysis proves that electricity price is a key parameter strongly affecting the final sustainability score. If supportive policies are adopted associated with electricity produced from WtE, this technology can experience a 16.2 % increase, making it an economic option for Lombok in the future.

Tuning on passive interfacial cooling of covalent organic framework hydrogel for enhancing freshwater and electricity generation

AbstractDeveloping an efficient freshwater and electricity co‐generation device (FECGD) can solve the shortage of freshwater and electricity. However, the poor salt resistance and refrigeration properties of the materials for FECGD put big challenges in the efficient and stable operation of these devices. To address these issues, we propose the covalent organic framework (COF) confined co‐polymerization strategy to prepare COF‐modified acrylamide cationic hydrogels (ACH‐COF), where hydrogen bonding interlocking between negatively charged polymer chains and COF pores can form a salt resistant hydrogel for stabilizing tunable passive interfacial cooling (TPIC). The FECPDs based on the TPIC and salt resistance of ACH‐COF display a maximum output power density of 2.28 W m−2, which is 4.3 times higher than that of a commercial thermoelectric generator under one solar radiation. The production rate of freshwater can reach 2.74 kg m−2 h−1. Our results suggest that the high efficiency and scalability of the FECGD can hold the promise of alleviating freshwater and power shortages.

SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region

The major problems in the power system network of the South Asian Association for Regional Cooperation (SAARC) region include the utilization of substantial fossil fuels, which leads to significant emissions of CO2. Moreover, due to the depletion of fossil fuels, it is very difficult to manage the demand requirement of the SAARC regions, which leads to significant power shortage issues. These problems would be resolved through the integration of the substantial amount of renewable energy resources (RERs) that are available in excess in the SAARC regions into the power system networks. This will provide a futuristic roadmap to a sustainable and green energy environment in the SAARC region. In this regard, the SAARC region has devised a new plan to transform its smart grid infrastructure into the super smart grid (SSG), which would connect the SAARC countries using the high voltage transmission lines utilizing each country's substantial RERs. To accelerate this ecofriendly effort, the key objective of this review study is to evaluate the potential of the SAARC SSG for demand management and emissions reduction. This study provides a comprehensive overview of SSG technology, covering its technical background, management, power flow improvement techniques, power stability techniques, simulation models, and future steps for development in the SAARC region. First, the regional conflicts, energy deficiency, and potential of the SAARC region have been discussed. Second, a potential architecture of the SAARC SSG has been simulated. Third, a novel Markov chain modeling has been presented for demand management in the SAARC regions. The results and discussions section presents findings from simulations and models. The paper also discusses CO2 reduction techniques, including methods for capturing, storing, and utilizing carbon in biofuel. The study also discusses futuristic optimization techniques in SSG, including blockchain, federated learning, reinforcement learning, the metaverse, digital twin technology, artificial intelligence, and cloud computing. The SSG challenges and limitations section summarizes the challenges and limitations faced by SSG. Finally, a roadmap to a sustainable and green energy environment for the SAARC power grids has been identified to support the implementation of the futuristic SAARC SSG. The results exhibited that there is a good potential for successful demand management and emissions reduction through the SAARC SSG.

Spatio-temporal adaptive algorithm using Hamming code based on 3D-MIMO wireless channel model

Space-time coding is an effective method for constructing communication systems while saving bandwidth, taking into account the shortage of frequency resources and power in fading channels, which is implemented based on the advantages of using adaptive antenna arrays. Currently, WiMAX radio access technology has become widespread; its use is justified in conditions of direct visibility between the base and mobile stations, but in a city with dense buildings and in the presence of organized interference, its use leads to an increase in the likelihood of bit errors. At the same time, the combination of OFDM (orthogonal frequency division multiplexing) and MIMO (spatial coding) technologies based on adaptive antenna arrays significantly reduces the probability of bit errors, and therefore increases the throughput of the communication system. The use of adaptive space-time devices at the outputs of antenna arrays makes it possible to select a signal path with maximum power and with a minimum error in receiving OFDM pilot subcarriers, even in the absence of direct visibility between the base and mobile stations and in conditions of active interference. In addition, the use of a 3D model of the communication channel will allow us to evaluate the effectiveness of the communication system model in real operating conditions. The goal of the work is to solve the problem of additionally increasing noise immunity in communication systems in conditions of numerous reflections caused by dense buildings in urban environments, using a combination of Hamming code, OFDM and an algorithm for adaptive spatio-temporal signal processing in receivers such as SISO (Singl Input – Singl Output), and MIMO (Multiply Input – Multiply Output), built on adaptive antenna arrays. The work calculated the weight coefficients of the adaptation algorithm using the theory of eigenvalues and eigenvectors of the spatial correlation matrix of signals at the outputs of the adaptive antenna arrays. Modeling of algorithms and a 3D model of the communication channel in Matlab using the Hamming code was carried out, as a result, an increase in the efficiency of error correction for the Hamming code was demonstrated, as well as a significant reduction in the probability of code errors and an increase in the performance of the communication system when using an adaptive algorithm. At the same time, the article uses the term “multipath” channel in contrast to the generally accepted, but incorrect “multibeam” channel, since multibeam can be a characteristic of an antenna system. The presented results confirm that when processing signals in a receiving system in a multipath channel with dense urban development, the use of adaptive spatiotemporal algorithms provides increased noise immunity and radio communication capacity, especially for image transmission when using unmanned aerial vehicles.

Research on the design and optimal control of the power take-off (PTO) system for underwater eel-type power generators

Wave energy and ocean current energy are considered stable, reliable, and highly predictable renewable energy sources. The development of ocean current energy conversion technology is crucial in addressing power shortages. However, the low velocity of most ocean currents worldwide, typically less than 1.5 m/s, poses a challenge for traditional ocean current energy capture devices. Current estimations of power generation from underwater devices often significantly differ from actual results. This study presents an eel-type power generation device designed for underwater use, investigates the design and optimization of a hydraulic power take-off (PTO) system suitable for such devices, and examines key components of the hydraulic PTO system like variable hydraulic motors and generators. Analytical research was conducted to understand its operational characteristics, with a focus on components such as the accumulator, speed control valve, and energy storage flywheel. The article also delves into the hydrodynamic and energy conversion characteristics of the device under shallow water wave and current conditions, enhancing the hydraulic PTO system model and establishing an integrated calculation model for real-time data transmission during the calculation process. Through evaluations of pitch angle and power generation under varying sea conditions, the study explores the impact of hydraulic motor displacement and damping coefficient on the hydrodynamic and power generation characteristics of the device, further validated through sea trials. The study findings indicate a high level of parameter adaptation among the components of the hydraulic system. The addition of an accumulator to the system results in smoother output response curves, suggesting that the accumulator absorbs impacts and stabilizes the hydraulic power transmission system. The proposed comprehensive calculation method enables a more precise prediction of the performance of the eel power generation device in real marine environments, capturing its movement behavior and power generation characteristics. Adjusting the motor displacement or damping coefficient under specific conditions can optimize the total damping of the hydraulic PTO system for maximum power output. The optimal power point of the hydraulic power generation system varies for different sea conditions, with a potential power generation efficiency of up to 80.3% under certain circumstances.

Optimal scheduling of the stand‐alone micro grids considering the reliability cost

AbstractIn order to prevent wastage of generated power of renewable resources, the energy storage systems can be utilized in the stand‐alone micro grids to store the excess produced power of the renewable generation units. When the generated power of the renewable resources is less than the required load, the energy storage systems can help to compensate all or part of the power shortage. In the current study, a stand‐alone micro grid including wind and tidal turbines, PV systems, batteries and fuel‐based generation units is considered to supply the required load of the micro grid. The generated power of each dispatch‐able generation units is determined in such a way as to minimize the operating cost. In the operating cost of the micro grid, the operating cost of the fuel‐based generation units and the reliability cost associated to the penalty of the curtailed loads are considered. To calculate the reliability cost of the micro grid, a comprehensive reliability evaluation of the micro grid considering the resource‐dependent failure rates for all composed components is performed. To study the effectiveness of the proposed reliability‐based scheduling approach, the numerical results associated to a stand‐alone micro grid containing wind, tidal, PV and fuel‐based generation units connected to the batteries are given.

A Two-Stage Operation Strategy for Energy Storage under Extreme-Heat-with-Low-Wind-Speed Scenarios of a Power System

Changes in weather conditions directly impact the output of wind power, photovoltaic systems, and other forms of uncontrollable power generation. During extreme weather events, the output from wind and photovoltaic sources is typically reduced. In light of this, this paper proposes a two-stage operational strategy for energy storage, under scenarios of extreme-heat-with-low-wind-speed, in power systems. Firstly, historical data on wind and solar power, along with weather characteristics, are collected to analyze the power output during multi-day periods of extreme heat and low wind speed. Then, Monte Carlo simulations are employed to generate multi-day load curves with inherent uncertainties, based on regional load characteristics of the power system. Finally, a two-stage operation strategy for energy storage charging and discharging is established. In the first stage, normal operations are conducted to identify periods of power shortage across various types of loads. In the second stage, based on the identified moments of power shortage from the first stage, charging and discharging constraints are applied to the energy storage systems. The feasibility and effectiveness of this two-stage operational strategy are then validated through simulations, using historical data to generate scenarios of multi-day extreme-heat-and-low-wind-speed conditions.

A data-driven optimization model for renewable electricity supply chain design

The rising electricity demand, driven by technological progress and population growth, has made the design of the electricity supply chain a major challenge. The transition to renewable energy sources has led to a substantial reduction in the costs and greenhouse gases emissions of electricity production. This study proposes a hybrid approach consisting of a recurrent neural network and an optimization model for designing a resilient electricity supply chain based on wind and solar energies. First, the electricity demand is forecast using the long short-term memory algorithm. Second, based on the forecast demand, a two-stage stochastic programming model is developed for designing an electricity supply chain under disruption risks. Partial and complete disruptions in power lines and facilities, including power plants, distribution and subtransmission substations, and low-power solar panel farms are considered. The objective of the proposed model is to minimize the total cost of the supply chain by making optimal decisions on facility location; technology selection for power plants; setting up transmission lines; and the quantity of power generation, power transmission, and power shortage in low- and high-voltage energy consumption systems. Three strategies are adopted to enhance the supply chain's resilience: multiple renewable energy sources, multiple power transmission lines, and lateral power lines. Lastly, the Sistan and Baluchestan Electric Power Distribution Company in Iran is used as a case study for this work to demonstrate the applicability of the proposed approach, analyze the findings, and derive relevant managerial insights.

Climate change impacts on the extreme power shortage events of wind-solar supply systems worldwide during 1980–2022

Economic productivity depends on reliable access to electricity, but the extreme shortage events of variable wind-solar systems may be strongly affected by climate change. Here, hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme shortage events worldwide. We find uptrends in extreme shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 4.1 hours (0.392 hours per year on average) between 1980–2000 and 2001–2022. However, such ascending trends are unevenly distributed worldwide, with a greater variability in low- and middle-latitude developing countries. This uptrend in extreme shortage events is driven by extremely low wind speed and solar radiation, particularly compound wind and solar drought, which however are strongly disproportionated. Only average 12.5% change in compound extremely low wind speed and solar radiation events may give rise to over 30% variability in extreme shortage events, despite a mere average 1.0% change in average wind speed and solar radiation. Our findings underline that wind-solar systems will probably suffer from weakened power security if such uptrends persist in a warmer future.

A self-regulation blades wind energy harvester system for self-powered wireless monitoring sensors

Using wind energy in the natural environment provides a promising solution for wireless sensor power supply for ecological, meteorological, environmental, and infrastructure monitoring. However, the uncertainty and disorder of natural wind restrict the further development of wind energy harvester systems and self-powered wireless sensor technology. Hence, this paper proposes a self-regulation blade wind energy harvester system (SBWEHS) for self-powered wireless monitoring sensors in remote field areas with power shortages. The system is mainly composed of three parts: wind harvesting mechanism, generator module, and energy storage module. The device can control the blade overlap ratio according to the wind speed while generating electricity to maximize the power coefficient. The system can control the blade’s closure in bad weather to protect the device. Based on the computational fluid dynamics technology of Ansys Fluent software, this study evaluated the impact of wind speed and blade overlap ratio on the two-stage blades. Experiments revealed that when the overlap ratio of the blades is fixed at 0.2 and the wind speed is set at 16 m/s, the maximum average power will reach 0.79 W, which fulfills the power requirements of wireless sensors. These results illustrate that the SBWEHS can effectively supply power for wireless monitoring sensors, especially in remote natural environments.

OPTIMAL PLACEMENT OF UNIFIED POWER FLOW CONTROLLER USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

The load growth in recent times due to advancements in technology and population increase has led to shortage of reactive power in the power system resulting in key issues such as voltage instability, large line losses, power outages among others. In this work, Particle Swarm Optimization (PSO) technique was used for optimal placement of Unified Power Flow Controller (UPFC) for power loss minimization using the IEEE 6-bus system as a test network. The load flow analysis was performed using Gauss-Seidel iterative method with and without inclusion of PSO-based UPFC. The PSO algorithm was used to find the optimum point for UPFC location and determined using the line with the least active power loss among other lines randomly selected by the PSO algorithm, with p.u defined as the voltage statutory limit. The simulation results revealed that the optimum point obtained was line 4-5, and the voltage profile were within the statutory limit with and without optimally placed UPFC. The total active power loss reduced from 495.5 to 273.9 MW, giving a reduction of 44.72 with optimally placed UPFC. The total reactive power loss equally reduced from 1439 to 936.2 MVAr, producing a reduction of 34.94 with optimally placed UPFC. These results obtained showed that PSO-based UPFC placement is suitable for power loss minimization and enhancement of the system voltage profile.

Economic Consequences Based on Reversible and Irreversible Degradation of PV Park in the Harsh Climate Conditions of Iraq

Photovoltaic (PV) system reliability and durability investigations are essential for industrial maturity and economic success. Recently, PV systems received much interest in Iraq due to many reasons—for instance, power shortage, global warming and pollution. Despite this interest, the precise economic implications of PV system reliability in harsh climates like Iraq remain uncertain. This work assesses the economic implications of PV system component reliability and soiling in Iraq using field experience and historical data. This study identifies the most common failure modes of PV systems installed in Iraq and similar climatic regions, and also ranks their severity. Simulations explore scenarios of PV module degradation rates, inverter lifetimes, soiling rates, and cleaning intervals, revealing that soiling has the most detrimental effect, with cleaning frequency leading to Levelized Cost of Electricity (LCOE) losses of over 30%, depending on the location. Inverter lifetime contributes to LCOE losses between 4 and 6%, depending on the PV system’s location. This study also evaluates the impact of tilt angle as a mitigation strategy for reducing soiling loss and its economic implications, finding that installing PV modules at higher tilt angles could reduce the economic impact of soiling by approximately 4.4%. Additionally, the optimal cleaning strategy identified is fully automated dry cleaning with robots, outperforming other strategies economically. Overall, the findings highlight that the LCOE in Iraq is relatively high compared to the global weighted average for utility-scale PV systems, primarily due to high soiling and degradation rates. The LCOE varies within the country, influenced by different degradation rates. This study aims to assist PV stakeholders in Iraq and the Middle East and North Africa (MENA) region in accurately estimating solar bankability; moreover, increasing reliability by minimizing the technical and financial risks by considering key parameters specific to these regions.

Features of multi-agent evaluation of of microgrid systems efficiency in parallel operation with the power system according to the reliability criterion

Ensuring reliable electricity supply to consumers in the face of destruction of critical energy infrastructure and shortage of generating capacities in Ukraine requires the development of distribution systems and their management systems. The purpose of the study was to substantiate the development of models for ensuring the optimal structure of electrical distribution networks under the conditions of connecting micro-networks according to the reliability criterion in conditions of limited investments. The paper uses a method for assessing the structural reliability of complex electrical systems with microgrid structures and forms a rational power distribution of such structures according to the criterion of optimising the reliability indicators of the studied electric power system. A mathematical optimisation model based on a computational system was proposed, designed to solve non-convex problems with minimising integral reliability indicators, considering financial constraints and the investment efficiency curve. Based on the research, the possibilities of optimisation using the BARON solver available on the NEOS server were examined. The results of the model’s performance are demonstrated using examples, considering the parameters of distribution system objects and their limitations on network components. An algorithm and programme for solving the problem of targeting power flows of microgrid structures in multi-node regional power systems are proposed. Algorithms and scenarios for the response of dispatching services are developed, provided that investments are limited, which will ensure the survivability of the power system as a whole. It is established that the development of rational electricity flows of microgrid structures will minimise the under-supply of electric energy by specific load nodes and determine their shares in covering the demand of the energy island in conditions of power shortage. The findings can be used in the operational management of power systems

The complex interplay between sectoral energy consumption and economic growth: Policy implications for Iran and beyond

Iran's abundant energy reserves starkly contrast with recent power and gas shortages, particularly impacting the industrial sector. Furthermore, long-term trends reveal a concerning pattern where total primary energy consumption has outpaced economic growth, doubling in recent decades. These challenges emphasize the need for a thorough evaluation of the intricate interplay between sectoral energy consumption and economic output in Iran, bearing profound policy implications. The current study employs ARDL and VECM approaches to analyze empirical long- and short-term dynamics. Regarding Iran, the results unveil causal relationships from industrial energy consumption to GDP and from GDP to energy consumption in buildings. Notably the significant positive value of elasticity of GDP with respect to industrial energy use highlights the need for nuanced energy management measures. Variations across sectors underscore the justification for recognizing industrial energy consumption as productive energy use. The results gain additional support from a panel data analysis spanning fourteen diverse countries, bearing significance for IAMs applied in climate change research. While IAMs traditionally employ total energy consumption or the sectoral energy uses collectively, as production factors, the research highlights the need to reevaluate model frameworks for potential different outcomes from established practices.

Cooking with unclean energy: insight into power shortage in rural areas

Cooking with unclean energy: insight into power shortage in rural areas

Optimizing decision-making in electric power system selection: A generalized approach based on Hamacher aggregation operators for q-rung orthopair fuzzy soft sets

Identifying the most suitable power source that is cost-effective, environmentally friendly, socially accepted, and capable of ensuring long-term sustainability represents a critical decision-making (DM) problem. Existing models based on traditional fuzzy sets and specific norms cannot describe complex issues such as selecting the best electric power system (EPS). These decision models can also be challenging, especially when transparency and regulatory compliance are required. This study introduces a novel operational framework based on Hamacher aggregation operators for q-rung orthopair fuzzy set soft sets. It aims to help decision-makers identify appropriate policies to overcome the shortfall of electric power. The advantages of the introduced decision support tool are better discrimination ability, well suited for handling extreme values in data, greater flexibility in representing complex DM scenarios, and parametrization. We also demonstrate the practical application of our approach to a real-world problem related to selecting the most suitable EPS in Pakistan. The selection is made from the set of five EPSs and is based on critical aspects like social acceptance, environmental impact, and cost-effectiveness. The findings show that the developed approach effectively identifies the best EPS. Tuning variables like q and ℓ allows for testing the sensitivity of the developed operators. The results showed that higher values of q make the uncertainty more evenly represented, which is essential for specific uses. The comparison with the existing approaches suggests the superiority of the proposed analytic framework.

Enhancing Ethiopian power distribution with novel hybrid renewable energy systems for sustainable reliability and cost efficiency

Economic development relies on access to electrical energy, which is crucial for society’s growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia’s Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95–1.025 p.u.). After reviewing the current distribution network’s operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system’s integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.